Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
Context-aware recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Analysis of cold-start recommendations in IPTV systems
Proceedings of the third ACM conference on Recommender systems
Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems
Proceedings of the third ACM conference on Recommender systems
Pairwise interaction tensor factorization for personalized tag recommendation
Proceedings of the third ACM international conference on Web search and data mining
Context-aware citation recommendation
Proceedings of the 19th international conference on World wide web
Proceedings of the fourth ACM conference on Recommender systems
Beyond accuracy: evaluating recommender systems by coverage and serendipity
Proceedings of the fourth ACM conference on Recommender systems
The YouTube video recommendation system
Proceedings of the fourth ACM conference on Recommender systems
Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation
Modeling and Learning Context-Aware Recommendation Scenarios Using Tensor Decomposition
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
Fast ALS-Based tensor factorization for context-aware recommendation from implicit feedback
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
| Hi-index | 0.00 |
Item-to-item recommendation -- when the most similar items sought to the actual item -- is an important recommendation scenario in practical recommender systems. One way to solve this task is to use the similarity between item feature vectors of factorization models. By doing so, one may transfer the well-known accuracy of factorization models observed at the personalized recommendations to the item-to-item case. This paper introduces context-awareness to item similarities in the factorization framework. Two levels of context-aware similarities are defined and applied to two context-aware implicit feedback based factorization methods (iTALS and iTALSx). We investigate the advantages and drawbacks of the approaches on four real life implicit feedback data sets and we characterize the conditions for their application. The results suggest that it is worth using contextual information for item-to-item recommendations in the factorization framework, however, one should carefully select the appropriate method to achieve similar accuracy gain than in the case of the more general item-to-user recommendation scenario.